• Proceedings of the Korean Society of Marine Engineers Conference
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• 2002.05a
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• pp.261-266
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• 2002

In this Paper, we Propose model-based tuning rules of the PID controller incorporating with genetic algorithms. Three sets of optimal PID parameters for step set-point tracking are obtained based on the first-order time delay model of plants and a genetic algorithm which minimizes performance indices(IAE, ISE and ITAE). Then tuning rules are obtained using the tuned parameter sets, potential rule models and a genetic algorithm. Simulation is carried out to verify the effectiveness of the proposed rules.

• ETRI Journal
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• v.39 no.4
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• pp.592-604
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• 2017

Intrusion detection is very important for network situation awareness. While a few methods have been proposed to detect network intrusion, they cannot directly and effectively utilize semi-quantitative information consisting of expert knowledge and quantitative data. Hence, this paper proposes a new detection model based on a directed acyclic graph (DAG) and a belief rule base (BRB). In the proposed model, called DAG-BRB, the DAG is employed to construct a multi-layered BRB model that can avoid explosion of combinations of rule number because of a large number of types of intrusion. To obtain the optimal parameters of the DAG-BRB model, an improved constraint covariance matrix adaption evolution strategy (CMA-ES) is developed that can effectively solve the constraint problem in the BRB. A case study was used to test the efficiency of the proposed DAG-BRB. The results showed that compared with other detection models, the DAG-BRB model has a higher detection rate and can be used in real networks.

• Journal of Biomedical Engineering Research
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• v.31 no.1
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• pp.40-49
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• 2010

A rule weight -based fuzzy classification model is proposed to analyze the patterns of admission-discharge of patients as a previous research for differential diagnosis of dyspnea. The proposed model is automatically generated from a labeled data set, supervised learning strategy, using three procedure methodology: i) select fuzzy partition regions from spatial distribution of data; ii) generate fuzzy membership functions from the selected partition regions; and iii) extract a set of candidate rules and resolve a conflict problem among the candidate rules. The effectiveness of the proposed fuzzy classification model was demonstrated by comparing the experimental results for the dyspnea patients' data set with 11 features selected from 55 features by clinicians with those obtained using the conventional classification methods, such as standard fuzzy classifier without rule weights, C4.5, QDA, kNN, and SVMs.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.11
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• pp.1456-1462
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• 2016

In this paper, we apply a chord progression learning model to a rule-based generation of a four-part chorus. The proposed system is given a 32-note melody line and completes the four-part chorus based on the rule of harmonics, predicting the chord progression with the CRBM model. The data for the training model was collected from various harmony textbooks, and chord progressions were extracted with key-independent features so as to utilize the given data effectively. It was shown that the output piece obtained with the proposed learning model had a more natural progression than the piece that used only the rule-based approach.

• The Transactions of the Korea Information Processing Society
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• v.6 no.9
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• pp.2281-2293
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• 1999

Many applications such as nuclear power plant monitoring, plant process control, stock market management, and network data management require a database system supporting both temporal data model and active rule processing. There have been some efforts to extend the temporal functionalities of the active database system, but an active database system based on temporal database, especially the one applied to the real application is rare. In this paper, we proposed an active temporal rule model based on bi-temporal database. And a rule language following the proposed rule model was described with its execution semantics. Then, how to apply to the nuclear power plant monitoring system was given as the examples.

• Journal of the Korea Society of Computer and Information
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• v.17 no.10
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• pp.25-33
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• 2012

In this paper, we propose a rule-based model to correct errors in a speech recognition result in the mobile device environment. The proposed model considers the mobile device environment with limited resources such as processing time and memory, as follows. In order to minimize the error correction processing time, the proposed model removes some processing steps such as morphological analysis and the composition and decomposition of syllable. Also, the proposed model utilizes the longest match rule selection method to generate one error correction candidate per point, assumed that an error occurs. For the purpose of deploying memory resource, the proposed model uses neither the Eojeol dictionary nor the morphological analyzer, and stores a combined rule list without any classification. Considering the modification and maintenance of the proposed model, the error correction rules are automatically extracted from a training corpus. Experimental results show that the proposed model improves 5.27% on the precision and 5.60% on the recall based on Eojoel unit for the speech recognition result.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.6
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• pp.10-21
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• 2013

This study suggested combined filtering model to eliminate outlier travel time data in transportation information system, and it was based on Median Absolute Deviation and Voting Rule. This model applied Median Absolute Deviation (MAD) method to follow normal distribution as first filtering process. After that, Voting rule is applied to eliminate remaining outlier travel time data after Median Absolute Deviation. In Voting Rule, travel time samples are judged as outliers according to travel-time difference between sample data and mean data. Elimination or not of outliers are determined using a majority rule. In case study of national highway No. 3, combined filtering model selectively eliminated outliers only and could improve accuracy of estimated travel time.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.470-479
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• 2016

The model based method for energy saving of the separately excited DC motor drive system is proposed in the paper. The accurate power loss model is necessary for this method. Therefore, the adaptive tabu search algorithm is applied to identify the parameters in the power loss model. The field current values for minimum power losses at any load torques and speeds are calculated by the proposed method. The rule based controller is used to control the field current and speed of the motor. The experimental results confirm that the model based method can successfully provide the energy saving for separately excited DC motor drive. The maximum value of the energy saving is 48.61% compared with the conventional drive method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.8
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• pp.832-841
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• 2009

Wheels of UGV can be used to get the information about the ground. However, wheels of UGV with actively articulated suspension cannot be used as the roles because the each wheel does not remain in contact with the ground. Therefore, in this study, we proposed the indexes and models to estimate the contact wheels. First, we formulated the dynamic equations about the actively articulated suspensions and wheels. Then estimation index $I_{WTC}$ and $I_{ATC}$ were developed from the equations, and analyzed the strengths and weaknesses of each index. As the results, we developed the fuzzy rule-based estimation model additionally derived from our observations. $I_{WTC}$ model and $I_{ATC}$ model could eliminate the noise of about 60% in comparison with the result without the estimation model. Fuzzy model also could reduce the noise of about 83%. In addition, fuzzy rule-based estimation model had high sensitivity and precision as well as robustness.

• Journal of KIISE:Databases
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• v.29 no.1
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• pp.1-14
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• 2002

Association rule discovery is a method of mining for the associated item set on large databases based on support and confidence threshold. The discovered association rules can be applied to the marketing pattern analysis in E-commerce, large shopping mall and so on. The association rule discovery makes multiple scan over the database storing large transaction data, thus, the algorithm requiring very high overhead might not be useful in real-time association rule discovery in dynamic environment. Therefore this paper proposes an active candidate set management model based on trigger and incremental update mechanism to overcome non-realtime limitation of association rule discovery. In order to implement the proposed model, we not only describe an implementation model for incremental updating operation, but also evaluate the performance characteristics of this model through the experiment.